Instructions to use NinedayWang/PolyCoder-2.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NinedayWang/PolyCoder-2.7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NinedayWang/PolyCoder-2.7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NinedayWang/PolyCoder-2.7B")
model = AutoModelForCausalLM.from_pretrained("NinedayWang/PolyCoder-2.7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NinedayWang/PolyCoder-2.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NinedayWang/PolyCoder-2.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NinedayWang/PolyCoder-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NinedayWang/PolyCoder-2.7B

SGLang

How to use NinedayWang/PolyCoder-2.7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NinedayWang/PolyCoder-2.7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NinedayWang/PolyCoder-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NinedayWang/PolyCoder-2.7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NinedayWang/PolyCoder-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NinedayWang/PolyCoder-2.7B with Docker Model Runner:
```
docker model run hf.co/NinedayWang/PolyCoder-2.7B
```

NinedayWang

urialon commited on Oct 21, 2022

Commit

84cbd49

1 Parent(s): 6523eda

Update README.md (#2)

Browse files

- Update README.md (4a682d03c7917b0228c5d312d44d52dba023745b)

Co-authored-by: Uri Alon <urialon@users.noreply.huggingface.co>

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README.md +4 -0

README.md CHANGED Viewed

@@ -3,6 +3,10 @@ presented in the paper ["A Systematic Evaluation of Large Language Models of Cod
 The model was trained on **249 GB** of code across **12** programming languages.
 For more information, see: [https://github.com/VHellendoorn/Code-LMs](https://github.com/VHellendoorn/Code-LMs)
 If you use this model, please cite:

 The model was trained on **249 GB** of code across **12** programming languages.
+**Note** - this model requires `transformers` version of at least **4.23.0**:
+```
+pip install transformers==4.23.0
+```
 For more information, see: [https://github.com/VHellendoorn/Code-LMs](https://github.com/VHellendoorn/Code-LMs)
 If you use this model, please cite: